/*
 *   This file is part of <open-parametrics>
 *   Copyright (c) 2006-2008 Miguel-Angel Sicilia
 *
 *   open-parametrics is free software: you can redistribute it and/or modify
 *   it under the terms of the Lesser GNU General Public License as
 *   published by the Free Software Foundation, either version 3 of
 *   the License, or (at your option) any later version.
 *
 *   open-parametrics is distributed in the hope that it will be useful,
 *   but WITHOUT ANY WARRANTY; without even the implied warranty of
 *   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *   GNU General Public License for more details.
 *
 *   You should have received a copy of the GNU General Public License
 *   along with open-parametrics.  If not, see <http://www.gnu.org/licenses/>.
 */

package es.uah.cc.ie.parametrics.regress;

import es.uah.cc.ie.parametrics.CERGenerator;
import es.uah.cc.ie.parametrics.CostDriver;
import es.uah.cc.ie.parametrics.CostEstimatingRelationship;
import es.uah.cc.ie.parametrics.Dataset;
import es.uah.cc.ie.parametrics.Variable;
import org.apache.commons.math.stat.regression.SimpleRegression;



/**
 * Defines the basic interface for a technique to generate CERs from
 * Datasets using linear regression.
 *
 *
 * @author Miguel-Angel Sicilia
 */
public  class SimpleLinearRegressionCERGenerator extends CERGenerator{

    /**
     * The underlying regression model.
     */
    private SimpleRegression _model;
    /**
     * Constructs the CERGenerator.
     */
    public SimpleLinearRegressionCERGenerator(String label){
        super(label);
    }

    /**
     * Creates a CER through linear regression on the instances in the
     * dataset.
     *
     * @param ds The dataset used for the generation
     * @return The CER
     */
    @Override
    public CostEstimatingRelationship generate(CostDriver[] x, Variable y,
            Dataset ds) {
       // this model supports only a cost driver:
        assert(x.length == 1);
       _model= new SimpleRegression();
       _model.addData(ds.getXYSlice(x[0], y));
       return new SimpleSingleLinearRegressionCER("generated",  _model);
    }


}